0). Preamble: Loading Packages and Data; Data Preperation

a). Load Packages

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.0.4
library(ggplot2)
library(ggcorrplot)
library(knitr)   # contains kable() function
library(partykit)
## Warning: package 'partykit' was built under R version 4.0.4
## Warning: package 'libcoin' was built under R version 4.0.4
library(caret)
library(rpart)
library(randomForest)
## Warning: package 'randomForest' was built under R version 4.0.4
library(pROC)
library(leaps)
library(glmnet)
library(gbm)
## Warning: package 'gbm' was built under R version 4.0.4

b). Import and Initial Data Prep

Be sure to set working directory to source file location

#bring in housing data
housing <- read.csv(file = "AmesHousing.csv", header = TRUE)

c). Preparing Dataset for Analysis

i). Removing NA values from dataset

# #BSMT Numeric--This may need to be addressed differently. You can have a basement without a bathroom, and zero values already exist.
# Bsmt.Full.Bath
# Bsmt.Half.Bath
#Garage.Yr.Blt data is a Year value may need to be adjusted.
#Mas.Vnr.Area

if (sum(is.na(housing)) >0) {
  
  DNE.Cols = c(74,75,76,32,33,34,35,37,8,59,65,66,62,60,29,30)
  zero.Cols = c(3, 5, 49,50,61,63,64,28)
  zero.Cols.alt = c(36,38,39,40) #These are all from one record (1342). Maybe just omit the record?
  catchall.cols = seq(1, 82, by=1)
  
  for (i in DNE.Cols){
    housing[[i]][is.na(housing[[i]])] = "DNE"
  }
  
  for (i in zero.Cols){
    housing[[i]][is.na(housing[[i]])] = 0
  }
  
  for (i in zero.Cols.alt){
    housing[[i]][is.na(housing[[i]])] = 0
  }
   
  # for (i in catchall.cols){
  #   housing[[i]][housing[[i]]==""] <- NA
  # }
  # 
  # for (i in catchall.cols){
  #   housing[[i]][is.na(housing[[i]])] = 0
  # }
  #Verify NAs are removed
  sum(is.na(housing))
}
## [1] 0

ii). Verify absences of NA values

sum(is.na(housing))
## [1] 0
#Show columns that have at least one NA
housing[, colSums(is.na(housing)) > 0]
## data frame with 0 columns and 2930 rows
#Show rowss that have at least one NA
housing[rowSums(is.na(housing)) > 0,]
##  [1] ï..Order        PID             MS.SubClass     MS.Zoning      
##  [5] Lot.Frontage    Lot.Area        Street          Alley          
##  [9] Lot.Shape       Land.Contour    Utilities       Lot.Config     
## [13] Land.Slope      Neighborhood    Condition.1     Condition.2    
## [17] Bldg.Type       House.Style     Overall.Qual    Overall.Cond   
## [21] Year.Built      Year.Remod.Add  Roof.Style      Roof.Matl      
## [25] Exterior.1st    Exterior.2nd    Mas.Vnr.Type    Mas.Vnr.Area   
## [29] Exter.Qual      Exter.Cond      Foundation      Bsmt.Qual      
## [33] Bsmt.Cond       Bsmt.Exposure   BsmtFin.Type.1  BsmtFin.SF.1   
## [37] BsmtFin.Type.2  BsmtFin.SF.2    Bsmt.Unf.SF     Total.Bsmt.SF  
## [41] Heating         Heating.QC      Central.Air     Electrical     
## [45] X1st.Flr.SF     X2nd.Flr.SF     Low.Qual.Fin.SF Gr.Liv.Area    
## [49] Bsmt.Full.Bath  Bsmt.Half.Bath  Full.Bath       Half.Bath      
## [53] Bedroom.AbvGr   Kitchen.AbvGr   Kitchen.Qual    TotRms.AbvGrd  
## [57] Functional      Fireplaces      Fireplace.Qu    Garage.Type    
## [61] Garage.Yr.Blt   Garage.Finish   Garage.Cars     Garage.Area    
## [65] Garage.Qual     Garage.Cond     Paved.Drive     Wood.Deck.SF   
## [69] Open.Porch.SF   Enclosed.Porch  X3Ssn.Porch     Screen.Porch   
## [73] Pool.Area       Pool.QC         Fence           Misc.Feature   
## [77] Misc.Val        Mo.Sold         Yr.Sold         Sale.Type      
## [81] Sale.Condition  SalePrice      
## <0 rows> (or 0-length row.names)

iii). Factors reorder

housing.factors <- housing

housing.factors$Exter.Cond <- factor(housing.factors$Exter.Cond, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Exter.Qual <- factor(housing.factors$Exter.Qual, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex')) 
housing.factors$Bsmt.Qual <- factor(housing.factors$Bsmt.Qual, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Bsmt.Cond <- factor(housing.factors$Bsmt.Cond, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Kitchen.Qual <- factor(housing.factors$Kitchen.Qual, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Fireplace.Qu <- factor(housing.factors$Fireplace.Qu, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Garage.Cond <- factor(housing.factors$Garage.Cond, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Garage.Qual <- factor(housing.factors$Garage.Qual, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Pool.QC <- factor(housing.factors$Pool.QC, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))
housing.factors$Heating.QC <- factor(housing.factors$Heating.QC, levels = c('DNE', 'Po', 'Fa', 'TA', 'Gd', 'Ex'))

housing.factors$MS.SubClass <- factor(housing.factors$MS.SubClass , levels = c(0, 20, 30, 40, 45, 50, 60, 70, 75, 80, 90, 120, 150, 160, 180, 190))

1. Exploratory Data Analysis

b). Correlation Plot

cor.housing.sales <- cor(numeric.df[,-1:-2])
ggcorrplot(cor.housing.sales)

Once the dataset has been scrubbed, initial exploratory data analysis is conducted. The first analysis is looking at all numeric vectors in the dataset and checking to see if there are any correlations between variables.

Correlations:

Total.Bsmt.SF ~ X1st.Flr.SF (0.80)

Gr.Liv.Area ~ TotRms.AbvGrd (0.81)

Garage.Cars ~ Garage.Area (0.89)

c). Box Plots of quals

qual.cols.vec <-  c(29, 30, 32, 33, 42, 55, 59, 65, 66, 74)
for (i in qual.cols.vec){
  boxplots <- qplot(data = housing.factors, x = housing.factors[,i], y = SalePrice, geom = "boxplot", xlab = paste(colnames(housing.factors)[i]))
  print(boxplots)
  
}

qual.cols.vec <-  c(3, 4, 7, 8 , 9 , 10, 11, 12, 13, 14, 15, 16, 17,
                    18, 23, 24, 25, 26, 27, 31, 35, 37, 41, 44, 57,
                    60, 62, 67, 75, 76, 80, 81)
for (i in qual.cols.vec){
  boxplots <- qplot(data = housing.factors, x = housing.factors[,i], y = SalePrice, geom = "boxplot", xlab = paste(colnames(housing.factors)[i]))
  print(boxplots)
  
}

#Note all variables that are used in models, so we can retain their box plots:

2. Sales Price Prediction Model (Khadija w/ assistance)

Tip: think about performance metrics other than MSE: MAE, relative error, etc. > A predictive model that uses housing sale prices 2006-2009.

a). Forward and Backward Stepwise Model

i). Finding the Optimal Stepwise Model

# Forward and backward stepwise (best subset will be too time-intensive)
nvmax = 1000 #ncol(housing)
model = map(
  .x = c('forward', 'backward'),
  .f = ~regsubsets(SalePrice ~ .,
           data = housing[housing$Yr.Sold != 2010, ],
           nvmax = nvmax, method = .x, really.big = F)
)
## Warning in leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in =
## force.in, : 14 linear dependencies found
## Reordering variables and trying again:
## Warning in rval$lopt[] <- rval$vorder[rval$lopt]: number of items to replace is
## not a multiple of replacement length

## Warning in rval$lopt[] <- rval$vorder[rval$lopt]: 14 linear dependencies found
## Reordering variables and trying again:
## Warning in rval$lopt[] <- rval$vorder[rval$lopt]: number of items to replace is
## not a multiple of replacement length
# Find optimal models
for(.x in 1:2) {
  num_variables = 1:length(summary(model[[.x]])$rss)
  # R^2
  plot_RSQ = ggplot(data = data.frame(summary(model[[.x]])$rsq),
                    aes(x = num_variables, y = summary(model[[.x]])$rsq)) +
    geom_line() +
    geom_point(x = which.max(summary(model[[.x]])$rsq),
               y = max(summary(model[[.x]])$rsq), aes(color = "red"),
               show.legend = FALSE) +
    xlab("# Variables") +
    ylab("R-squared") +
    theme_bw()
  # RSS
  plot_RSS = ggplot(data = data.frame(summary(model[[.x]])$rss),
                    aes(x = num_variables, y = summary(model[[.x]])$rss)) +
    geom_line() +
    geom_point(x = which.min(summary(model[[.x]])$rss),
               y = min(summary(model[[.x]])$rss), aes(color = "red"),
               show.legend = FALSE) +
    xlab("# Variables") +
    ylab("RSS") +
    theme_bw()
  # BIC
  plot_BIC = ggplot(data = data.frame(summary(model[[.x]])$bic),
                    aes(x = num_variables, y = summary(model[[.x]])$bic)) +
    geom_line() +
    geom_point(x = which.min(summary(model[[.x]])$bic),
               y = min(summary(model[[.x]])$bic), aes(color = "red"),
               show.legend = FALSE) +
    xlab("# Variables") +
    ylab("BIC") +
    ggtitle("BIC", 
            str_c("Optimal value at ", which.min(summary(model[[.x]])$bic))) + 
    theme_bw()
  # AIC
  plot_AIC = ggplot(data = data.frame(summary(model[[.x]])$cp),
                    aes(x = num_variables, y = summary(model[[.x]])$cp)) +
    geom_line() +
    geom_point(x = which.min(summary(model[[.x]])$cp),
               y = min(summary(model[[.x]])$cp), aes(color = "red"),
               show.legend = FALSE) +
    xlab("# Variables") +
    ylab("AIC") +
    ggtitle("AIC", 
            str_c("Optimal value at ", which.min(summary(model[[.x]])$cp))) + 
    theme_bw()
  # Plot
  gridExtra::grid.arrange(plot_RSQ, plot_RSS, plot_AIC, plot_BIC, 
                           ncol = 2, nrow = 2)
}

ii). Predictions of the Optimal Model

# Get predictions from optimal models
model_coef = map(1:2, 
                 ~coef(model[[.x]], which.min(summary(model[[.x]])$bic)))
temp = bind_rows(
  tibble(method = 'forward', var = names(model_coef[[1]]), 
         coef = model_coef[[1]], varmatch = NA_character_),
  tibble(method = 'backward', var = names(model_coef[[2]]), 
         coef = model_coef[[2]], varmatch = NA_character_)
) 

# Try to figure out programmatically which variables are in the models
for(r in 1:nrow(temp)) {
  if(temp$method[r] == 'forward') {
    potential_names = names(housing)[agrepl(names(model_coef[[1]][r]),
                                            names(housing), 
                                            max.distance = 0.2)]
  } else {
    potential_names = names(housing)[agrepl(names(model_coef[[2]][r]),
                                            names(housing),
                                            max.distance = 0.2)]
  }
  if(length(potential_names) == 0) {
    varmatch = NA_character_
  } else if(length(potential_names) == 1) {
    varmatch = potential_names
  } else {
    varmatch = str_flatten(
      intersect(
        unlist(strsplit(potential_names, '')), 
        unlist(strsplit(potential_names, ''))
      )
    )
  }
  temp$varmatch[r] = varmatch
}

iii). Refitting Forward and Backaward Models

# Refit: Forward
forward_predictors = c('MS.SubClass', 'Lot.Area', 'Lot.Shape', 'Lot.Config',
                       'Land.Slope', 'Neighborhood', 'Condition.1',
                       'Condition.2', 'Bldg.Type', 'Overall.Qual',
                       'Overall.Cond', 'Year.Built', 'Roof.Matl', 
                       'Exterior.1st', 'Mas.Vnr.Type', 'Exter.Qual',
                       'Foundation', 'Bsmt.Qual', 'BsmtFin.Type.1',
                       'BsmtFin.SF.2', 'Heating.QC', 'Kitchen.Qual',
                       'TotRms.AbvGrd', 'Functional', 'Fireplaces', 
                       'Fireplace.Qu', 'Garage.Type', 'Garage.Qual',
                       'Paved.Drive', 'Open.Porch.SF', 'Misc.Feature',
                       'Misc.Val', 'Mo.Sold', 'Yr.Sold', 'Sale.Type',
                       'BsmtFin.Type.1', 'Garage.Finish', 'Garage.Cond')
forward_pred = predict(
  lm(formula(paste('SalePrice ~', str_flatten(forward_predictors, "+"))), 
     data = housing), #[housing$Yr.Sold != 2010, ]),
  housing[housing$Yr.Sold == 2010, ]
)
## Warning in predict.lm(lm(formula(paste("SalePrice ~",
## str_flatten(forward_predictors, : prediction from a rank-deficient fit may be
## misleading
# Refit: Backward
backward_predictors = c('MS.SubClass', 'MS.Zoning', 'Lot.Area', 'Land.Contour',
                        'Neighborhood', 'Condition.1', 'Condition.2',
                        'Bldg.Type', 'Overall.Qual', 'Overall.Cond', 
                        'Year.Built', 'Year.Remod.Add', 'Roof.Matl', 
                        'Exterior.1st', 'Mas.Vnr.Type', 'Exter.Qual', 
                        'Exter.Cond', 'Bsmt.Qual', 'BsmtFin.Type.1', 
                        'BsmtFin.SF.2', 'Heating.QC', 'Kitchen.Qual',
                        'Functional', 'Fireplaces', 'Fireplace.Qu', 
                        'Garage.Type', 'Garage.Qual', 'Paved.Drive', 
                        'Open.Porch.SF', 'Pool.QC', 'Misc.Feature', 'Mo.Sold',
                        'Sale.Type', 'BsmtFin.Type.1', 'Garage.Cond')
backward_pred = predict(
  lm(formula(paste('SalePrice ~', str_flatten(backward_predictors, "+"))), 
     data = housing), #[housing$Yr.Sold != 2010, ]),
  housing[housing$Yr.Sold == 2010, ]
)
## Warning in predict.lm(lm(formula(paste("SalePrice ~",
## str_flatten(backward_predictors, : prediction from a rank-deficient fit may be
## misleading

b). Generalized Linear Model

# GLM
glm = glm(SalePrice ~ ., data = housing[housing$Yr.Sold != 2010, ])
names(coef(glm)[coef(summary(glm))[, "Pr(>|t|)"]  < 0.05])[-1]
##  [1] "Land.ContourHLS"      "Lot.ConfigCulDSac"    "Land.SlopeMod"       
##  [4] "Land.SlopeSev"        "NeighborhoodClearCr"  "NeighborhoodEdwards" 
##  [7] "NeighborhoodGrnHill"  "NeighborhoodMitchel"  "NeighborhoodNAmes"   
## [10] "NeighborhoodNoRidge"  "NeighborhoodNridgHt"  "NeighborhoodNWAmes"  
## [13] "NeighborhoodStoneBr"  "Condition.1Norm"      "Condition.1PosA"     
## [16] "Condition.1PosN"      "Condition.2PosA"      "Condition.2PosN"     
## [19] "Bldg.TypeDuplex"      "Bldg.TypeTwnhs"       "Bldg.TypeTwnhsE"     
## [22] "Overall.Qual"         "Overall.Cond"         "Year.Built"          
## [25] "Year.Remod.Add"       "Roof.MatlCompShg"     "Roof.MatlMembran"    
## [28] "Roof.MatlMetal"       "Roof.MatlRoll"        "Roof.MatlTar&Grv"    
## [31] "Roof.MatlWdShake"     "Roof.MatlWdShngl"     "Exterior.1stBrkFace" 
## [34] "Exterior.1stPreCast"  "Mas.Vnr.TypeBrkFace"  "Mas.Vnr.TypeStone"   
## [37] "Exter.QualFa"         "Exter.QualGd"         "Bsmt.QualDNE"        
## [40] "Bsmt.ExposureMn"      "Bsmt.ExposureNo"      "BsmtFin.Type.1ALQ"   
## [43] "BsmtFin.SF.1"         "BsmtFin.Type.2ALQ"    "Total.Bsmt.SF"       
## [46] "Heating.QCGd"         "Heating.QCPo"         "Heating.QCTA"        
## [49] "ElectricalFuseF"      "ElectricalMix"        "ElectricalSBrkr"     
## [52] "X1st.Flr.SF"          "X2nd.Flr.SF"          "Low.Qual.Fin.SF"     
## [55] "Bsmt.Half.Bath"       "Kitchen.QualFa"       "Kitchen.QualGd"      
## [58] "TotRms.AbvGrd"        "FunctionalMaj2"       "FunctionalMin2"      
## [61] "FunctionalSal"        "Garage.TypeAttchd"    "Garage.TypeBasment"  
## [64] "Garage.TypeCarPort"   "Garage.TypeDNE"       "Garage.FinishDNE"    
## [67] "Garage.QualDNE"       "Garage.QualPo"        "Garage.CondDNE"      
## [70] "Wood.Deck.SF"         "Misc.FeatureTenC"     "Mo.Sold"             
## [73] "Sale.ConditionFamily"
#glm.pred = predict(glm, housing[housing$Yr.Sold == 2010,])

c). Lasso Model

# Lasso [Primary]
# Define x and y objects for Lasso, as well as specifying training data
train = which(housing$Yr.Sold != 2010)
x = model.matrix(SalePrice ~ ., housing)[, -1]
y = housing$SalePrice

# Fit and plot model
lasso_model = cv.glmnet(x[train, ], y[train] , alpha = 1)
plot(lasso_model)

i). Minimun Lambda and 1SE Value

# Find min and 1SE values of lambda
lasso_model$lambda.min; lasso_model$lambda.1se
## [1] 466.3576
## [1] 4349.267
lasso_pred = predict(lasso_model, s = lasso_model$lambda.1se, 
                     newx = x[-train, ])

ii). Refit Lasso Model and Find Non-Zero Coefficients

# Refit model and find non-zero coefficients
lasso_model_coef = map(
  .x = c(lasso_model$lambda.min, lasso_model$lambda.min),
  .f = ~predict(glmnet(x, y ,alpha = 1), s = .x, type = 'coefficients')
)

d). Tree-based and Boosted Models

boost_model = gbm(SalePrice ~ ., 
                  data = mutate_if(housing, is.character, as.factor)[housing$Yr.Sold != 2010, ],
                  distribution = 'gaussian', 
                  n.trees = 5000,
                  cv.folds = 10,
                  shrinkage = 0.1,
                  interaction.depth = 4)

i). Summary Table of Boosted Model

kable(summary(boost_model))

var rel.inf
Overall.Qual Overall.Qual 37.1286842
Gr.Liv.Area Gr.Liv.Area 13.1948513
Neighborhood Neighborhood 12.0713215
Total.Bsmt.SF Total.Bsmt.SF 5.7865133
Garage.Cars Garage.Cars 4.4933549
X1st.Flr.SF X1st.Flr.SF 2.8650289
BsmtFin.SF.1 BsmtFin.SF.1 2.6287936
Kitchen.Qual Kitchen.Qual 2.3419824
Lot.Area Lot.Area 1.8353530
X2nd.Flr.SF X2nd.Flr.SF 1.5178139
Garage.Area Garage.Area 1.0045378
Year.Remod.Add Year.Remod.Add 0.9900508
Fireplace.Qu Fireplace.Qu 0.8302846
Garage.Finish Garage.Finish 0.7819557
Bsmt.Unf.SF Bsmt.Unf.SF 0.7622139
Mas.Vnr.Area Mas.Vnr.Area 0.7474918
Overall.Cond Overall.Cond 0.6552541
Year.Built Year.Built 0.6436722
Garage.Type Garage.Type 0.6346365
Sale.Condition Sale.Condition 0.6238393
Bsmt.Qual Bsmt.Qual 0.5791012
Exterior.1st Exterior.1st 0.5503620
ï..Order ï..Order 0.4815629
Open.Porch.SF Open.Porch.SF 0.4719893
Lot.Frontage Lot.Frontage 0.4597990
BsmtFin.Type.1 BsmtFin.Type.1 0.4506541
Exterior.2nd Exterior.2nd 0.4464177
Bsmt.Exposure Bsmt.Exposure 0.3250711
Wood.Deck.SF Wood.Deck.SF 0.2997144
PID PID 0.2919512
Garage.Yr.Blt Garage.Yr.Blt 0.2875590
Sale.Type Sale.Type 0.2840957
Mo.Sold Mo.Sold 0.2755276
Full.Bath Full.Bath 0.2571520
Fireplaces Fireplaces 0.2527253
Condition.1 Condition.1 0.2395266
Central.Air Central.Air 0.2315695
Exter.Qual Exter.Qual 0.2178743
Screen.Porch Screen.Porch 0.2028794
TotRms.AbvGrd TotRms.AbvGrd 0.1630385
Functional Functional 0.1481984
Land.Contour Land.Contour 0.1386179
Lot.Config Lot.Config 0.1087015
Condition.2 Condition.2 0.0906090
Mas.Vnr.Type Mas.Vnr.Type 0.0905139
Enclosed.Porch Enclosed.Porch 0.0793467
Bsmt.Cond Bsmt.Cond 0.0760394
MS.SubClass MS.SubClass 0.0686401
Heating.QC Heating.QC 0.0581087
BsmtFin.Type.2 BsmtFin.Type.2 0.0558533
Roof.Style Roof.Style 0.0521694
Bedroom.AbvGr Bedroom.AbvGr 0.0515129
House.Style House.Style 0.0480243
Roof.Matl Roof.Matl 0.0459648
Bldg.Type Bldg.Type 0.0451744
MS.Zoning MS.Zoning 0.0439773
X3Ssn.Porch X3Ssn.Porch 0.0432444
Lot.Shape Lot.Shape 0.0419323
Land.Slope Land.Slope 0.0389543
Bsmt.Full.Bath Bsmt.Full.Bath 0.0358541
Foundation Foundation 0.0356138
BsmtFin.SF.2 BsmtFin.SF.2 0.0310351
Alley Alley 0.0308349
Exter.Cond Exter.Cond 0.0286965
Half.Bath Half.Bath 0.0257873
Low.Qual.Fin.SF Low.Qual.Fin.SF 0.0216602
Garage.Qual Garage.Qual 0.0200783
Fence Fence 0.0198098
Kitchen.AbvGr Kitchen.AbvGr 0.0183110
Garage.Cond Garage.Cond 0.0164053
Heating Heating 0.0159849
Electrical Electrical 0.0142772
Paved.Drive Paved.Drive 0.0136198
Misc.Val Misc.Val 0.0132649
Bsmt.Half.Bath Bsmt.Half.Bath 0.0104347
Misc.Feature Misc.Feature 0.0087168
Yr.Sold Yr.Sold 0.0053319
Pool.QC Pool.QC 0.0024961
Street Street 0.0000000
Utilities Utilities 0.0000000
Pool.Area Pool.Area 0.0000000

ii). Relative Influence Chart

for(.x in 1:5) {
  plot(boost_model, i = summary(boost_model)[.x, 'var'])
}

boost_pred = predict(boost_model, 
                     newdata = housing[housing$Yr.Sold == 2010, ],
                     n.trees = 5000)
## Warning in predict.gbm(boost_model, newdata = housing[housing$Yr.Sold == : NAs
## introduced by coercion

3. Performance Evaluation

a). Performance Metrics Function

# Create performance_metrics function
performance_metrics = function(actual = housing[housing$Yr.Sold == 2010, 'SalePrice'], predicted, plot = T, by_neighborhood = F) {
  # Check that actual and predicted values are the same length
  if(length(actual) != length(predicted)) {
    message("'actual' and 'predicted' vectors must be the same length.")
  } else {
    # Compute metrics (MSE, RMSE, MAE, relative error)
    # Including by neighborhood
    if(by_neighborhood) {
      metrics = tibble(
        neighborhood = housing[housing$Yr.Sold == 2010, 'Neighborhood'],
        actual = actual,
        predicted = as.numeric(predicted),
        mse = (actual - predicted)^2,
        mae = abs(actual - predicted),
        rel_error = (predicted - actual) / actual
      ) %>% 
        group_by(neighborhood) %>% 
        summarize(across(mse:rel_error, mean)) %>% 
        ungroup()
      
      # Plot
      barplot = ggplot(data = pivot_longer(temp, cols = mse:rel_error,
                                           names_to = 'performance_metric',
                                           values_to = 'metric_score'),
                    aes(x = performance_metric, y = metric_score)) + 
        geom_bar(stat = 'identity') + 
        geom_text(aes(label = round(metric_score, 3))) + 
        xlab("Metric") +
        ylab("Score") + 
        facet_wrap(~neighborhood) + 
        theme_bw()
      scatter = ggplot(data = tibble(
        neighborhood = housing[housing$Yr.Sold == 2010, 'Neighborhood'],
        actual = actual, 
        predicted = predicted), 
        aes(x = actual, y = predicted)) + 
        geom_point(aes(size = abs(actual - predicted)), alpha = 0.4) + 
        xlab("Actual") + 
        ylab("Predicted") + 
        facet_wrap('neighborhood') +
        geom_abline(slope = 1, intercept = 0, col = 'red') + 
        theme_bw()
    } else {
      # Compute error and create data frame
      mse = mean((actual - predicted)^2)
      mae = mean(abs(actual - predicted))
      rel_error = mean((predicted - actual) / actual)
      metrics = tibble(mse = mse, mae = mae, rel_error = rel_error)
      
      # Plot
      barplot = ggplot(data = tibble(mse, mae, rel_error) %>% 
                      pivot_longer(cols = everything(),
                                   names_to = 'performance_metric',
                                   values_to = 'metric_score'),
                    aes(x = performance_metric, y = metric_score)) + 
        geom_bar(stat = 'identity') + 
        geom_text(aes(label = round(metric_score, 3))) + 
        xlab("Metric") +
        ylab("Score") + 
        theme_bw()
      scatter = ggplot(data = tibble(actual = actual, 
                                     predicted = predicted), 
                       aes(x = actual, y = predicted)) + 
        geom_point(aes(size = abs(actual - predicted)), alpha = 0.4) + 
        xlab("Actual") + 
        ylab("Predicted") + 
        geom_abline(slope = 1, intercept = 0, col = 'red') + 
        theme_bw()
    }
    
    # Return objects
    if(plot) {
      return(list(metrics = metrics, barplot = barplot, scatter = scatter))
    } else {
      return(list(metrics = metrics))
    }
  }
}

b). Comparing Accuracy of Prediction Estimates Against Actual Values in 2010.

i). Forward Stepwise Model Accuracy

# Use performance_metrics function defined above for all models
performance_metrics(actual = housing[housing$Yr.Sold == 2010, 'SalePrice'],forward_pred, plot = T, by_neighborhood = F)
## $metrics
## # A tibble: 1 x 3
##          mse    mae rel_error
##        <dbl>  <dbl>     <dbl>
## 1 679905223. 18382.    0.0156
## 
## $barplot

## 
## $scatter

ii).Bakcward Stepwise Model Accuracy

# Use performance_metrics function defined above for all models
performance_metrics(actual = housing[housing$Yr.Sold == 2010, 'SalePrice'], backward_pred, plot = T, by_neighborhood = F)
## $metrics
## # A tibble: 1 x 3
##          mse    mae rel_error
##        <dbl>  <dbl>     <dbl>
## 1 842466277. 20881.    0.0267
## 
## $barplot

## 
## $scatter

iii). GLM Model Accuracy

# Use performance_metrics function defined above for all models
#performance_metrics(actual = housing[housing$Yr.Sold == 2010, 'SalePrice'], glm.pred, plot = T, by_neighborhood = F)

iv). Lasso Model Accuracy

# Use performance_metrics function defined above for all models
performance_metrics(actual = housing[housing$Yr.Sold == 2010, 'SalePrice'], lasso_pred, plot = T, by_neighborhood = F)
## $metrics
## # A tibble: 1 x 3
##          mse    mae rel_error
##        <dbl>  <dbl>     <dbl>
## 1 847049101. 19762.    0.0519
## 
## $barplot

## 
## $scatter

v). Boosted Model Accuracy

# Use performance_metrics function defined above for all models
performance_metrics(actual = housing[housing$Yr.Sold == 2010, 'SalePrice'], boost_pred, plot = T, by_neighborhood = F)
## $metrics
## # A tibble: 1 x 3
##           mse    mae rel_error
##         <dbl>  <dbl>     <dbl>
## 1 1230481776. 25206.     0.113
## 
## $barplot

## 
## $scatter

c). Home Features Relation to the Model

i). Home Features That are Most Predictive of Sales Price.

#Code Here

ii). How do the Above Features Relate to Sales Price?

#Code Here

4. The Model’s accuracy accros Neighbourhoods

#Code Here

5. Renovation Value Calculator (Evan and Anthony)

I am going to write a bit about the calculator

What it does

Data sources:

Room Renovation Costs Business Insider

Roof Renovation Cost Super Storm Restoration

reno_value = function(upgrade = c('add_full_bathroom', 'remodel_kitchen',
                                  'finish_basement', 'replace_roof'),
                      budget = c('low', 'medium', 'high')) { #Evan fix to allow null renovation and one budget type
  
  # # Confirm upgrade and budget args; otherwise throw an error
  upgrade = tryCatch(
    match.arg(upgrade, choices = c('add_bathroom', 'remodel_kitchen',
                                  'finish_basement', 'replace_roof'),
                      several.ok = T),
    error = identity
  )
  budget = tryCatch(
    match.arg(budget, choices = c('low', 'medium', 'high'), several.ok = F),
    error = identity
  )
  
  # Handle errors
  if(str_detect(upgrade, "Error") | str_detect(budget, "Error")) {
      suppressWarnings(return(message(upgrade)))
      break()
  } else {
    # Update data based on stated upgrades
    remodeled_housing = mutate(housing, Reno.Cost = 0)
    
    # Add bathroom
    if('add_full_bathroom' %in% upgrade) {
      remodeled_housing = mutate(
        remodeled_housing,
        budget = budget,
        Reno.Cost = case_when(
          budget == 'low' ~ Reno.Cost + 5957,
          budget == 'medium' ~ Reno.Cost + 10349,
          budget == 'high' ~ Reno.Cost + 14833,
          T ~ Reno.Cost
        ),
        Full.Bath = Full.Bath + 1,
        Year.Remod.Add = 2010
      )
    }
    
    # Remodel kitchen
    if('remodel_kitchen' %in% upgrade) {
      message('Note: Remodeling kitchens that are already of excellent quality (i.e., "Ex") will not increase the estimated sale price.')
      remodeled_housing = mutate(
        remodeled_housing,
        Reno.Cost = case_when(
          Kitchen.Qual == 'Ex' ~ Reno.Cost,
          budget == 'low' ~ Reno.Cost + 12557,
          budget == 'medium' ~ Reno.Cost + 23556,
          budget == 'high' ~ Reno.Cost + 34789,
          T ~ Reno.Cost
        ),
        Kitchen.Qual = case_when(
          Kitchen.Qual == 'Gd' ~ 'Ex',
          Kitchen.Qual == "TA" & budget == 'low' ~ 'Gd',
          Kitchen.Qual == "TA" ~ 'Ex',
          Kitchen.Qual == 'Fa' & budget == 'low' ~ "TA",
          Kitchen.Qual == 'Fa' & budget == 'medium' ~ "Gd",
          Kitchen.Qual == 'Fa' & budget == 'high' ~ 'Ex',
          Kitchen.Qual == 'Po' & budget == 'low' ~ 'Fa',
          Kitchen.Qual == 'Po' & budget == 'medium' ~ "TA",
          Kitchen.Qual == 'Po' & budget == 'high' ~ 'Gd',
          T ~ Kitchen.Qual
        ),
        Year.Remod.Add = 2010
      )
    }
    
    # Finish basement
    if('finish_basement' %in% upgrade) {
      message('Note: Properties without a basement obviously cannot have finished basements, so this renovation will not increase their estimated sale price.')
      remodeled_housing = mutate(
        remodeled_housing,
        Reno.Cost = case_when(
          Bsmt.Cond %in% c("DNE", '') ~ Reno.Cost,
          budget == 'low' ~ Reno.Cost + 0.75 * 15000,
          budget == 'medium' ~ Reno.Cost + 15000,
          budget == 'high' ~ Reno.Cost + 1.25 * 15000,
          T ~ Reno.Cost
        ),
        BsmtFin.Type.1 = case_when(
          Bsmt.Cond %in% c("DNE", '') ~ BsmtFin.Type.1,
          budget == 'low' ~ "BLQ",
          budget == 'medium' ~ "ALQ",
          budget == 'high' ~ "GLQ",
          T ~ BsmtFin.Type.1
        ),
        BsmtFin.SF.1 = BsmtFin.SF.1 + Bsmt.Unf.SF,
        Year.Remod.Add = 2010
      )
    }
    
    # Replace roof
    if('replace_roof' %in% upgrade) {
      remodeled_housing = mutate(
        remodeled_housing,
        Reno.Cost = case_when(
          budget == 'low' ~ Reno.Cost + 5100,
          budget == 'medium' ~ Reno.Cost + 10000,
          budget == 'high' ~ Reno.Cost + 15000,
          T ~ Reno.Cost
        ),
        Exter.Cond = case_when(
          Exter.Cond == 'Gd' ~ 'Ex',
          Exter.Cond == "TA" & budget == 'low' ~ 'Gd',
          Exter.Cond == "TA" ~ 'Ex',
          Exter.Cond == "Fa" & budget == 'low' ~ "TA",
          Exter.Cond == "Fa" & budget == 'medium' ~ 'Gd',
          Exter.Cond == "Fa" & budget == 'high' ~ 'Ex',
          Exter.Cond == "Po" & budget == 'low' ~ "Fa",
          Exter.Cond == "Po" & budget == 'medium' ~ "TA",
          Exter.Cond == "Po" & budget == 'high' ~ 'Gd',
          T ~ Exter.Cond
        ),
        Year.Remod.Add = 2010
      )
    }
    
    # Predict based on new features
    costs_benefits = tibble(
      orig_predicted_sale_price = as.numeric(lasso_pred),
      new_predicted_sale_price = predict(
        boost_model, 
        newdata = remodeled_housing[remodeled_housing$Yr.Sold == 2010, ],
        n.trees = 5000
      ),
      remodeling_costs = remodeled_housing[remodeled_housing$Yr.Sold == 2010,
                                           'Reno.Cost'],
      net = new_predicted_sale_price - orig_predicted_sale_price - remodeling_costs
    ) 
    
    # Return costs and predictions
    # return(list(remodeled_housing = remodeled_housing,
    #             costs_benefits = costs_benefits))
    
    
    
    if('add_full_bathroom' %in% upgrade) {
        return(kable(list("remodeled_housing" =
                          remodeled_housing[c("ï..Order", "PID", "Full.Bath",
                                            "Year.Remod.Add", "SalePrice",
                                            "Reno.Cost")],
                          "cost_benefits" = costs_benefits)))
    } else if ('remodel_kitchen' %in% upgrade){
        return(kable(list("remodeled_housing"=
                          remodeled_housing[c("ï..Order", "PID", "Kitchen.Qual",
                                            "Year.Remod.Add", "SalePrice",
                                            "Reno.Cost")],
                          "cost_benefits" = costs_benefits)))
    } else if ('finish_basement' %in% upgrade){
        return(kable(list("remodeled_housing"=
                          remodeled_housing[c("ï..Order", "PID", "BsmtFin.SF.1",
                                            "Year.Remod.Add", "SalePrice",
                                            "Reno.Cost")],
                          "cost_benefits" = costs_benefits)))
    } else if('replace_roof' %in% upgrade) {
        return(kable(list("remodeled_housing"=
                          remodeled_housing[c("ï..Order", "PID", "Exter.Cond",
                                            "Year.Remod.Add", "SalePrice",
                                            "Reno.Cost")],
                          "cost_benefits" = costs_benefits)))
    }else{
        return(kable(list("remodeled_housing"=
                        remodeled_housing[c("ï..Order", "PID", "Full.Bath",
                                            "Kitchen.Qual", "BsmtFin.SF.1",
                                            "Exter.Cond", "Year.Remod.Add",
                                            "SalePrice", "Reno.Cost")],
                        "cost_benefits" = costs_benefits)))      
    }
                                  
           
  }
}

Using the Renovation Calculator

reno_value('add_bathroom', 'high')
## Warning in predict.gbm(boost_model, newdata =
## remodeled_housing[remodeled_housing$Yr.Sold == : NAs introduced by coercion
ï..Order PID Full.Bath Kitchen.Qual BsmtFin.SF.1 Exter.Cond Year.Remod.Add SalePrice Reno.Cost
1 526301100 1 TA 639 TA 1960 215000 0
2 526350040 1 TA 468 TA 1961 105000 0
3 526351010 1 Gd 923 TA 1958 172000 0
4 526353030 2 Ex 1065 TA 1968 244000 0
5 527105010 2 TA 791 TA 1998 189900 0
6 527105030 2 Gd 602 TA 1998 195500 0
7 527127150 2 Gd 616 TA 2001 213500 0
8 527145080 2 Gd 263 TA 1992 191500 0
9 527146030 2 Gd 1180 TA 1996 236500 0
10 527162130 2 Gd 0 TA 1999 189000 0
11 527163010 2 TA 0 TA 1994 175900 0
12 527165230 2 TA 935 Gd 2007 185000 0
13 527166040 2 TA 0 TA 1998 180400 0
14 527180040 1 Gd 637 TA 1990 171500 0
15 527182190 1 Gd 368 TA 1985 212000 0
16 527216070 3 Ex 1416 TA 2003 538000 0
17 527225035 2 TA 427 TA 2005 164000 0
18 527258010 1 Ex 1445 TA 2010 394432 0
19 527276150 1 TA 120 TA 1951 141000 0
20 527302110 2 TA 790 TA 1988 210000 0
21 527358140 2 TA 705 TA 1977 190000 0
22 527358200 2 Gd 885 TA 1974 170000 0
23 527368020 2 Gd 533 TA 2000 216000 0
24 527402200 1 TA 578 TA 1970 149000 0
25 527402250 1 TA 734 TA 2008 149900 0
26 527403020 1 TA 775 TA 1968 142000 0
27 527404120 1 TA 804 TA 1970 126000 0
28 527425090 1 TA 432 TA 1971 115000 0
29 527427230 2 Gd 1051 TA 1999 184000 0
30 527451180 1 TA 156 TA 1971 96000 0
31 527451330 1 TA 300 TA 1971 105500 0
32 527451410 1 TA 0 TA 1971 88000 0
33 527452190 2 TA 360 TA 1977 127500 0
34 527453130 2 Gd 514 TA 1975 149900 0
35 527453150 1 TA 0 TA 1975 120000 0
36 527454200 2 TA 311 TA 1978 146000 0
37 528108120 2 Ex 0 TA 2010 376162 0
38 528112020 2 Gd 1218 TA 2008 306000 0
39 528120060 2 Ex 1646 TA 2010 395192 0
40 528120090 2 Gd 0 TA 2010 290941 0
41 528120100 2 Gd 1201 TA 2005 220000 0
42 528120120 2 Ex 110 TA 2005 275000 0
43 528138020 2 Ex 28 TA 2006 259000 0
44 528138060 2 Gd 2 TA 2004 214000 0
45 528150070 2 Ex 2188 TA 2010 611657 0
46 528175010 2 Gd 733 TA 2010 224000 0
47 528176010 2 Ex 1373 TA 2004 500000 0
48 528176070 2 Gd 578 TA 2002 320000 0
49 528180070 2 Ex 456 TA 2005 319900 0
50 528181050 2 Gd 24 TA 2006 205000 0
51 528188120 2 Gd 0 TA 2003 175500 0
52 528218150 2 Gd 0 TA 2005 199500 0
53 528228285 2 Gd 16 TA 2006 160000 0
54 528228440 2 Gd 24 TA 2005 192000 0
55 528231020 2 Gd 326 TA 2004 184500 0
56 528240070 2 Gd 625 TA 2002 216500 0
57 528240130 2 Gd 0 TA 2004 185088 0
58 528250100 2 TA 250 TA 2000 180000 0
59 528292020 2 Gd 0 TA 2001 222500 0
60 528328100 2 Gd 0 TA 1999 333168 0
61 528344070 2 Gd 0 TA 1998 355000 0
62 528358010 2 Gd 919 TA 1996 260400 0
63 528363130 2 Gd 1032 TA 1995 325000 0
64 528382020 2 Gd 524 TA 2007 290000 0
65 528382030 2 TA 0 TA 1998 221000 0
66 528386040 2 Gd 816 TA 1996 410000 0
67 528445060 2 Gd 0 TA 2006 221500 0
68 528456160 2 Gd 0 TA 2010 204500 0
69 528458070 2 Gd 24 TA 2009 215200 0
70 528458110 2 Gd 1078 TA 2008 262500 0
71 528477080 2 Gd 222 TA 2005 254900 0
72 528480090 2 Gd 1414 TA 2005 271500 0
73 531367050 2 Gd 656 TA 2004 233000 0
74 531369060 2 Gd 0 TA 2004 181000 0
75 531380080 2 Gd 695 TA 2002 205000 0
76 531451110 2 TA 543 TA 1980 143000 0
77 531451280 2 Gd 0 TA 1992 189000 0
78 531452020 2 TA 0 TA 1979 99500 0
79 531452050 1 TA 623 TA 1984 125000 0
80 531452180 2 Gd 402 TA 2003 194500 0
81 531453010 2 TA 338 TA 1985 152000 0
82 531453040 2 TA 0 TA 1981 171000 0
83 531477050 1 TA 0 Fa 1950 67500 0
84 532351140 2 TA 0 TA 1978 112000 0
85 532354090 1 Gd 899 TA 1995 148000 0
86 532376160 1 TA 553 Gd 2009 138500 0
87 532377060 1 TA 450 Gd 1967 122000 0
88 532377100 1 TA 824 TA 1963 133000 0
89 532378110 1 TA 659 Gd 2010 127000 0
90 532479070 2 TA 126 Gd 2002 169000 0
91 533130020 2 Gd 674 TA 1992 190000 0
92 533130130 2 Gd 250 TA 1993 362500 0
93 533130160 2 Gd 1129 TA 1993 285000 0
94 533205060 2 Gd 1298 TA 2005 260000 0
95 533208090 2 Gd 0 TA 2004 190000 0
96 533210060 2 Gd 280 TA 2004 155000 0
97 533212010 2 Gd 0 TA 2005 151000 0
98 533212020 2 Gd 368 TA 2005 149500 0
99 533212060 2 Gd 376 TA 2005 152000 0
100 533213030 2 Gd 578 TA 2009 222000 0
101 533221090 2 Gd 378 TA 2000 177500 0
102 533221110 2 Gd 466 TA 2000 177000 0
103 533223020 2 Gd 604 TA 1999 155000 0
104 533223100 2 Gd 244 TA 2003 147110 0
105 533238045 1 Gd 1032 TA 2010 267916 0
106 533244030 2 Gd 484 TA 2000 254000 0
107 533253060 1 Gd 728 TA 1980 155000 0
108 533253210 1 Gd 1052 TA 1978 206000 0
109 533352170 2 Gd 533 TA 2005 130500 0
110 534102025 2 Gd 833 TA 2002 230000 0
111 534129040 2 TA 506 TA 1967 218500 0
112 534151175 2 Gd 1137 TA 1993 243500 0
113 534152050 2 Gd 1200 TA 2001 205000 0
114 534152070 3 TA 687 TA 1978 212500 0
115 534152100 2 TA 394 TA 1988 196500 0
116 534176060 1 Gd 982 TA 2006 197500 0
117 534176230 1 TA 329 TA 1971 171000 0
118 534202160 1 TA 698 TA 1966 142250 0
119 534251320 1 TA 569 TA 1963 143000 0
120 534276360 1 TA 569 TA 1959 128950 0
121 534401110 1 TA 1059 TA 1966 159000 0
122 534401140 2 Gd 1010 TA 1967 178900 0
123 534403360 2 TA 0 TA 1964 136300 0
124 534403410 1 TA 1014 TA 1964 180500 0
125 534425310 1 Gd 763 Gd 1963 137500 0
126 534427010 2 TA 1500 TA 2001 84900 0
127 534430080 1 Gd 49 TA 1996 142125 0
128 534431050 2 TA 670 TA 2002 197600 0
129 534431070 2 TA 300 Gd 1950 172500 0
130 534450180 1 TA 696 Gd 2008 116500 0
131 534451150 1 Fa 0 Po 1966 76500 0
132 534475100 1 TA 354 TA 1954 128000 0
133 534476260 1 TA 674 TA 1972 153000 0
134 534479320 1 TA 540 TA 1954 132000 0
135 535101020 1 Gd 944 TA 2008 178000 0
136 535106190 2 TA 443 TA 1960 154300 0
137 535125010 2 TA 912 TA 1989 180000 0
138 535126040 2 Gd 247 TA 2002 190000 0
139 535152010 2 TA 0 TA 1958 135000 0
140 535152050 2 Gd 1188 TA 2009 214000 0
141 535152130 1 TA 856 TA 1959 136000 0
142 535152150 1 Gd 1018 TA 1959 165500 0
143 535152250 1 TA 922 Gd 1957 145000 0
144 535153070 1 TA 0 TA 1959 148000 0
145 535154050 1 TA 1000 TA 1959 142000 0
146 535175070 1 TA 697 TA 1960 167500 0
147 535175180 1 TA 936 TA 1959 108538 0
148 535179020 1 TA 734 TA 1959 159500 0
149 535180130 1 TA 0 TA 1956 108000 0
150 535302080 1 Fa 339 TA 1952 135000 0
151 535302200 1 TA 648 Gd 1955 122500 0
152 535304060 1 TA 532 TA 1958 119000 0
153 535304180 1 Gd 731 Gd 2007 109000 0
154 535325400 1 TA 0 TA 1994 105000 0
155 535327140 1 TA 0 TA 2007 107500 0
156 535350030 1 TA 320 TA 2003 144900 0
157 535350040 1 TA 248 TA 2000 129000 0
158 535353190 1 TA 0 TA 1950 97500 0
159 535377090 0 TA 1056 TA 1980 144000 0
160 535401080 1 Gd 72 Gd 2006 162000 0
161 535425010 2 Gd 0 Gd 2007 242000 0
162 535450210 1 TA 481 TA 2004 132000 0
163 535453070 1 Gd 340 TA 1994 154000 0
164 535454070 2 TA 507 Fa 1952 166000 0
165 535456110 1 Gd 234 TA 2000 134800 0
166 535457010 1 TA 0 TA 1962 160000 0
167 535457090 1 TA 588 TA 1960 148000 0
168 535476350 1 TA 717 TA 1984 192000 0
169 535477020 1 TA 0 TA 1966 155000 0
170 902102100 1 TA 48 TA 1954 80400 0
171 902104060 1 TA 0 TA 2004 96500 0
172 902106130 1 TA 579 TA 1950 109500 0
173 902106140 1 TA 0 TA 1950 115000 0
174 902110090 1 Gd 274 TA 1957 143000 0
175 902125080 2 TA 0 Gd 1950 107400 0
176 902125160 1 TA 0 Gd 1950 80000 0
177 902128170 1 Gd 510 Gd 1992 119000 0
178 902206040 1 TA 234 TA 2001 130000 0
179 902206130 1 Fa 0 TA 2000 119000 0
180 902206220 1 Gd 780 Gd 2009 129000 0
181 902206240 1 TA 176 TA 1962 100000 0
182 902207130 1 TA 0 Fa 1970 12789 0
183 902207140 1 TA 0 Gd 2003 105900 0
184 902305110 2 Gd 0 Gd 2003 150000 0
185 902327040 2 TA 686 Gd 1950 139000 0
186 902329030 3 Gd 0 Gd 2002 240000 0
187 902401060 2 Fa 0 Fa 1950 76500 0
188 902402010 1 TA 600 TA 1996 149700 0
189 902402130 1 TA 0 TA 1950 125500 0
190 902402250 2 TA 0 TA 1950 122500 0
191 902403070 1 TA 283 TA 1994 140750 0
192 903204040 1 TA 788 Gd 1997 128500 0
193 903206120 1 Gd 474 TA 2005 209500 0
194 903225150 1 Gd 188 TA 1950 87000 0
195 903226160 1 TA 452 TA 1950 134000 0
196 903227020 1 TA 264 Gd 1950 128000 0
197 903227150 1 Gd 360 TA 1950 132000 0
198 903228150 1 TA 300 TA 1950 139900 0
199 903232190 1 TA 276 TA 1980 123900 0
200 903233120 2 TA 448 TA 1950 138400 0
201 903234050 1 TA 960 TA 1950 109500 0
202 903425110 2 TA 0 TA 1950 140000 0
203 903426180 1 TA 0 Fa 1950 149500 0
204 903426200 1 Gd 0 Gd 2009 159900 0
205 903430080 1 TA 0 Gd 1993 122000 0
206 903450110 1 TA 104 TA 1963 110000 0
207 903454060 2 Fa 0 Fa 1950 55000 0
208 903476030 2 Gd 0 Gd 1996 140000 0
209 904100140 2 TA 0 Gd 1950 244400 0
210 904101110 2 TA 766 TA 1959 173000 0
211 904300150 1 Po 0 TA 1950 107500 0
212 904302260 1 TA 1026 Fa 1950 100000 0
213 904350045 1 TA 368 TA 1950 95000 0
214 904351040 1 TA 73 TA 1950 93369 0
215 904352190 2 TA 736 Gd 1950 114900 0
216 905101100 1 TA 0 Gd 1996 94000 0
217 905101300 2 TA 704 Fa 1967 136000 0
218 905101310 2 TA 0 TA 1968 136500 0
219 905102060 1 TA 841 TA 1965 131500 0
220 905103060 1 TA 1302 TA 1996 121500 0
221 905104210 1 TA 842 TA 1967 125000 0
222 905105070 1 Gd 188 Gd 2001 154000 0
223 905105200 1 Gd 240 TA 2008 137900 0
224 905106140 1 TA 371 TA 1969 158000 0
225 905106170 1 TA 775 TA 1968 137250 0
226 905107110 1 TA 1319 TA 1978 160250 0
227 905107250 1 TA 267 Gd 1968 163000 0
228 905107320 2 TA 438 TA 1978 158900 0
229 905109130 2 Gd 1201 TA 1997 328000 0
230 905109170 1 TA 1092 Gd 2001 270000 0
231 905201030 1 TA 0 TA 1950 85000 0
232 905202090 2 Gd 442 TA 1995 128000 0
233 905325030 0 Gd 1258 TA 1979 260000 0
234 905352140 2 Gd 964 TA 1992 230000 0
235 905378040 2 Fa 288 TA 1990 124000 0
236 905426060 1 TA 108 TA 1950 83000 0
237 905426200 1 Gd 104 TA 1987 144500 0
238 905451320 1 TA 739 TA 1956 129000 0
239 905452110 1 TA 192 TA 1979 127000 0
240 905452160 1 TA 954 TA 1998 128000 0
241 905453040 2 Gd 36 TA 2009 186000 0
242 905453080 1 Gd 1346 TA 2010 308030 0
243 905475510 2 TA 0 Fa 1964 114000 0
244 905478190 1 Gd 0 TA 2006 84900 0
245 906201030 2 Gd 0 TA 1985 178000 0
246 906201120 1 Gd 1433 TA 1987 270000 0
247 906204230 2 Gd 860 Gd 1993 218000 0
248 906223090 2 Gd 750 TA 1994 236000 0
249 906225210 1 Gd 747 TA 1992 147000 0
250 906378100 2 Gd 0 TA 2008 245350 0
251 906380190 2 Gd 686 TA 2006 206000 0
252 906385010 2 Gd 0 TA 2009 198900 0
253 906385020 2 Gd 24 TA 2010 187000 0
254 906402200 3 Gd 1470 TA 1990 320000 0
255 906425045 2 TA 0 TA 1993 138500 0
256 906477110 1 TA 504 TA 1971 155000 0
257 907125090 1 TA 870 Gd 1996 159000 0
258 907180050 2 TA 353 TA 1998 191000 0
259 907185060 2 Gd 505 TA 1998 200500 0
260 907200280 1 TA 198 Gd 2004 150000 0
261 907200290 1 TA 378 TA 1977 161750 0
262 907200340 1 TA 182 TA 1976 128200 0
263 907203070 1 TA 480 TA 1972 127000 0
264 907250030 2 Gd 1682 TA 2003 318000 0
265 907252120 2 Gd 1358 TA 2002 272000 0
266 907262050 2 Gd 483 TA 2003 237000 0
267 907280040 2 Gd 0 TA 1997 240000 0
268 907290040 2 Gd 0 TA 1998 224900 0
269 907290170 1 Gd 672 TA 2003 143750 0
270 907290240 1 Gd 662 TA 2004 143000 0
271 907401090 2 Gd 370 TA 2003 232000 0
272 907410080 2 Gd 712 TA 2005 213000 0
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2726 905202190 1 TA 810 TA 1967 278000 0
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2930 924151050 2 TA 758 TA 1994 188000 0
orig_predicted_sale_price new_predicted_sale_price remodeling_costs net
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121690.35 144138.6 0 22448.22059
163486.57 177502.4 0 14015.85978
244894.28 222200.6 0 -22693.63279
167936.70 183882.1 0 15945.37230
185605.88 183200.7 0 -2405.17601
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210188.50 209770.4 0 -418.14283
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95923.25 135494.5 0 39571.20770
216844.91 205745.1 0 -11099.79820
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153166.34 157376.9 0 4210.52811
148657.52 157332.5 0 8675.01675
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110842.87 136822.3 0 25979.39772
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203010.69 187588.9 0 -15421.73932
121418.17 130147.7 0 8729.57678
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164913.58 154154.4 0 -10759.21603
136139.33 149025.8 0 12886.44883
158603.35 152928.1 0 -5675.29190
335914.21 322144.6 0 -13769.63008
309667.10 268760.0 0 -40907.09067
345843.48 341942.6 0 -3900.86293
248965.16 239924.1 0 -9041.02692
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301262.96 241242.5 0 -60020.41705
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395624.45 356404.7 0 -39219.70448
220881.95 193169.0 0 -27712.96435
408909.17 370274.3 0 -38634.87910
294262.98 290267.7 0 -3995.24949
309684.79 255500.9 0 -54183.85072
227124.91 211710.4 0 -15414.54842
202383.16 174799.3 0 -27583.89010
174163.76 175970.5 0 1806.74897
177495.00 167907.2 0 -9587.76772
203156.70 195715.4 0 -7441.34433
187363.67 174494.3 0 -12869.38031
207821.25 196082.7 0 -11738.56734
202220.65 186347.0 0 -15873.63860
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306888.26 310900.0 0 4011.75519
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248135.02 217229.8 0 -30905.20927
344879.29 289138.8 0 -55740.47897
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220903.25 198903.0 0 -22000.28613
337918.68 357828.4 0 19909.76293
240412.59 227324.9 0 -13087.67107
191446.92 188810.4 0 -2636.54929
193001.74 190633.0 0 -2368.77830
239613.92 252276.9 0 12663.00755
221278.98 193495.9 0 -27783.05682
242115.01 207155.6 0 -34959.36928
225276.45 202935.9 0 -22340.50546
198157.83 183464.2 0 -14693.66812
233004.50 212182.2 0 -20822.31418
136243.80 148545.6 0 12301.75686
191112.62 182967.4 0 -8145.20430
130013.78 141215.5 0 11201.73183
124735.76 140343.2 0 15607.41307
203529.67 196687.5 0 -6842.18160
152557.34 156844.4 0 4287.07149
165843.62 159851.0 0 -5992.65216
109335.25 132083.6 0 22748.38802
167842.19 154664.6 0 -13177.59885
150280.58 158502.1 0 8221.54789
129251.48 158444.3 0 29192.84466
114778.27 137740.9 0 22962.60356
125928.19 147316.5 0 21388.35817
130849.88 159740.0 0 28890.16609
194226.03 182711.6 0 -11514.38965
237132.45 198114.6 0 -39017.83360
283037.12 273446.5 0 -9590.61257
285681.37 256590.2 0 -29091.14547
253922.09 195522.8 0 -58399.32431
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174956.62 163899.0 0 -11057.59633
168820.82 162276.7 0 -6544.11152
174138.84 164532.3 0 -9606.55837
161823.42 150887.2 0 -10936.20784
257052.28 203333.6 0 -53718.72942
174835.83 163363.6 0 -11472.22356
183301.39 172481.0 0 -10820.36560
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166261.43 158741.7 0 -7519.74366
282880.07 252605.4 0 -30274.67944
233105.82 225453.5 0 -7652.32577
190218.25 177474.6 0 -12743.69147
217025.30 198707.6 0 -18317.68078
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215422.23 200461.4 0 -14960.79321
192434.17 179721.4 0 -12712.73435
236132.47 199204.3 0 -36928.18676
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197859.22 182813.8 0 -15045.43227
188649.20 169860.3 0 -18788.90005
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144730.48 152461.4 0 7730.95958
176767.94 167691.8 0 -9076.15996
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147094.82 155812.7 0 8717.84020
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166333.90 196135.4 0 29801.45341
130118.58 155831.7 0 25713.13758
211180.47 190202.5 0 -20978.01597
165682.86 165648.4 0 -34.44185
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87035.66 117345.4 0 30309.71484
120655.80 143918.2 0 23262.44824
175000.52 169946.3 0 -5054.21940
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151419.52 154637.2 0 3217.67348
220513.82 208463.7 0 -12050.13830
132288.88 149678.1 0 17389.20868
175494.75 182413.4 0 6918.70353
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135648.40 149781.5 0 14133.10769
186371.20 187199.5 0 828.26219
105976.31 134586.2 0 28609.89957
122508.20 149192.5 0 26684.28652
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147750.41 156690.0 0 8939.54465
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